The present invention relates to the image processing arts. It finds particular application in conjunction with improving the diagnostic value of magnetic resonance medical diagnostic images and will be described with particular reference thereto. It is to be appreciated that the invention will also find application in conjunction with improving other medical diagnostic images such as CT, digital x-ray, nuclear camera, ultrasound, and the like, as well as non-medical electronic images such as television images, radio telescope, and the like.
Medical diagnostic images are commonly subject to degradation from noise, system imperfections, and the like. One technique for improving diagnostic images is to acquire redundant data, e.g. the data for two or more images and averaging or summing to reduce the effects of random noise or error. However, multiple data acquisitions are time consuming and expensive. Moreover, in regions of patient motion, averaging may blur rather than improve the resultant image.
Others have attempted to improve the imagability of the subject by injecting contrast agents into the patient. See for example, U.S. Pat. No. 4,834,964 of Rosen. However, injected contrast agents only improve limited image characteristics. As an invasive technique, it is sometimes inappropriate for medical reasons.
The acquired images can be processed with modifications to the histogram or distribution of signal values on a global or local basis. See for example, U.S. Pat. No. 5,063,607 of FitzHenry. In other techniques, the gray scale range of each subimage region is stretched such that it covers the entire display range. See for example, U.S. Pat. No. 4,991,092. However, histogram modifications by attempting to expand the dynamic range of the data, increase the noise in the image. Local histogram modifications cause a blocking effect on the resultant image. That is, the processing of the various subregions of the image with different histogram modifications tends to result in a lack of uniformity over the entire image.
Others have also enhanced images using convolution or filtering techniques. Such techniques include the selective amplification of selected frequency bands as illustrated in U.S. Pat. No. 5,072,314 of Chang. Others have used a combination of high and low pass filtering to enhance images as illustrated for example in U.S. Pat. No. 5,081,692 to Kwon or U.S. Pat. No. 4,972,256 to Hirosawa. However, global filtering techniques tend to blur the images and eliminate the lower frequency regions. This makes evaluation of the images difficult.
To eliminate some of the drawbacks of a global filtering or convolution, others have used locally adjusted filtering. See for example, U.S. Pat. No. 4,761,819 of Denison, et al., U.S. Pat. No. 4,991,092 of Kwon, U.S. Pat. No. 5,050,227 of Furusawa, and "Adaptive Smoothing: A General Tool For Early Vision", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 13, No. 6, Jun. 1991, Saint-Marc, et al. However, the local filtering techniques had difficulty distinguishing between sudden image variations attributable to edges and sudden image variations attributable to noise. These techniques also fail to account for differences in edge direction and regional variance, producing an image which is overly smooth without consideration of the edges. That is, they tend to blur the image.
Still others have attempted restoration approaches in which the acquisition process was modeled and the degradations of the imaging process described mathematically. These techniques then attempted to invert the degradations using restoration techniques such as least squares, Bayesian, or Kahlman filtering. However, the restoration methods required a model for the acquisition process. Complicated acquisition processes, such as MRI imaging, were too difficult to model accurately. Moreover, computing the parameters of a complicated model for a given image can require iterative algorithms which have a great computational expense.
U.S. Pat. No. 4,691,366 of Fenster, et al. uses filters which are adjusted to enhance long edges and attenuate noise and points. However, this techniques requires an analysis of the imaging system in order to produce the appropriate filters. Such analysis is time consuming and prone to errors.
The present invention provides a new and improved imaging technique which overcomes the above-referenced problems and others.